A GOVERNANCE-ORIENTED PROMPT ENGINEERING FRAMEWORK FOR AI-ASSISTED INTERNAL AUDITING

Abstract

The diffusion of generative Artificial Intelligence (AI) across organizational environments is reshaping both the objects of internal audit and the tools auditors use during assurance work. Although prior research has discussed AI adoption in auditing and the risks associated with large language models (LLMs), less attention has been paid to how auditors should structure, document, and govern their own interaction with these systems. This study adopts a conceptual synthesis design and integrates three literature domains: internal audit standards and governance guidance, LLM risk and security frameworks, and prompt engineering / human–AI interaction research. Based on this synthesis, the study develops a governance-oriented framework that links 13 audit-specific prompt types to the main phases of the internal audit lifecycle: planning, fieldwork, reporting, and follow-up. The proposed framework treats prompts and AI-assisted outputs not as informal productivity aids, but as reviewable working-paper artifacts subject to evidentiary discipline, human validation, and documentation controls. The analysis indicates that structured prompting can improve repeatability, transparency, and professional skepticism by making assumptions explicit, constraining unsupported inference, and strengthening the linkage between AI-generated text and auditor-supplied evidence. The article contributes to the literature by reframing prompt engineering as a governable audit competency and by providing a lifecycle-based prompt taxonomy for practical implementation. It also clarifies that LLM output should be used as intermediate analytical support rather than as audit evidence or authoritative judgment.